Low rates of vaccination, emergence of novel variants of SARS-CoV-2, and increasing transmission relating to seasonal changes leave many U.S. communities at risk for surges of COVID-19 during the winter and spring of 2022 that might strain hospital capacity, as in previous waves. The trajectories of COVID-19 hospitalizations during this period are expected to differ across communities depending on their age distributions, vaccination coverage, cumulative incidence, and adoption of risk mitigating behaviors. Yet, existing predictive models of COVID-19 hospitalizations are almost exclusively focused on national- and state-level predictions. This leaves local policymakers in urgent need of tools that can provide early warnings about the possibility that COVID-19 hospitalizations may rise to levels that exceed local capacity. In this work, we develop simple decision rules to predict whether COVID-19 hospitalization will exceed the local hospitalization capacity within a 4- or 8-week period if no additional mitigating strategies are implemented during this time. These decision rules use real-time data related to hospital occupancy and new hospitalizations associated with COVID-19, and when available, genomic surveillance of SARS-CoV-2. We showed that these decision rules present reasonable accuracy, sensitivity, and specificity (all ≥80%) in predicting local surges in hospitalizations under numerous simulated scenarios, which capture substantial uncertainties over the future trajectories of COVID-19 during the winter and spring of 2022. Our proposed decision rules are simple, visual, and straightforward to use in practice by local decision makers without the need to perform numerical computations.

The incidence of dermatologic infections in patients receiving checkpoint inhibitors (CPIs) has not been systematically described.

Many individuals who survive tuberculosis disease face ongoing disability and elevated mortality risks. However, the impact of post-tuberculosis sequelae is generally omitted from policy analyses and disease burden estimates. We therefore estimated the global burden of tuberculosis, inclusive of post-tuberculosis morbidity and mortality.

We present a new concept, , and apply it to the COVID-19 non-pharmaceutical interventions (NPI) in two epicenters of the pandemic: Mexico and Brazil. Punt Politics refers to national leaders in federal systems deferring or deflecting responsibility for health systems decision-making to sub-national entities without evidence or coordination. The fragmentation of authority and overlapping functions in federal, decentralized political systems make them more susceptible to coordination problems than centralized, unitary systems. We apply the concept to pandemics, which require national health system stewardship, using sub-national NPI data that we developed and curated through the Observatory for the Containment of COVID-19 in the Americas to illustrate Punt Politics in Mexico and Brazil. Both countries suffer from protracted, high levels of COVID-19 mortality and inadequate pandemic responses, including little testing and disregard for scientific evidence. We illustrate how populist leadership drove Punt Politics and how partisan politics contributed to disabling an evidence-based response in Mexico and Brazil. These cases illustrate the combination of decentralization and populist leadership that is most conducive to punting responsibility. We discuss how Punt Politics reduces health system functionality, providing lessons for other countries and future pandemic responses, including vaccine rollout.